MACHINE LEARNING TECHNIQUES - LASA

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Notice that, if we use the identity function for f and g, we find again the classical Hebbian rule.
Recall that, if the two variables are uncorrelated, we have { 1 2}
independent we have E( f ( y ) f ( y )) E( f ( y )) E( f ( y ))
1 2 1 2
E y , y = 0, and that if they are
= for any given function f. The
network must, thus, converge to a solution that satisfies the later condition.
Figure 6-13: ICA with anti-Hebbian learning applied to two images that have been mixed together. After a
number of iterations the network converges to a correct separation of the two source images.
[DEMOS\ICA\ICA_IMAGE_MIX.M]
© A.G.Billard 2004 – Last Update March 2011